Computer Science > Information Theory
arXiv:2411.18579 (cs)
[Submitted on 27 Nov 2024]
Title:Surveying the space of descriptions of a composite system with machine learning
View a PDF of the paper titled Surveying the space of descriptions of a composite system with machine learning, by Kieran A. Murphy and 2 other authors
View PDFHTML (experimental)Abstract:Multivariate information theory provides a general and principled framework for understanding how the components of a complex system are connected. Existing analyses are coarse in nature -- built up from characterizations of discrete subsystems -- and can be computationally prohibitive. In this work, we propose to study the continuous space of possible descriptions of a composite system as a window into its organizational structure. A description consists of specific information conveyed about each of the components, and the space of possible descriptions is equivalent to the space of lossy compression schemes of the components. We introduce a machine learning framework to optimize descriptions that extremize key information theoretic quantities used to characterize organization, such as total correlation and O-information. Through case studies on spin systems, Sudoku boards, and letter sequences from natural language, we identify extremal descriptions that reveal how system-wide variation emerges from individual components. By integrating machine learning into a fine-grained information theoretic analysis of composite random variables, our framework opens a new avenues for probing the structure of real-world complex systems.
| Comments: | Code here:this https URL |
| Subjects: | Information Theory (cs.IT); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2411.18579 [cs.IT] |
| (orarXiv:2411.18579v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2411.18579 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Surveying the space of descriptions of a composite system with machine learning, by Kieran A. Murphy and 2 other authors
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